Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database

Enteric methane (CH) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive da...

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Detalles Bibliográficos
Autores: Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., Bayat, A.R., Brito, A. F., Boland, T., Casper, D. P., Crompton, L. A., Dijkstra, J., Eugène, M. A., Garnsworthy, P. C., Haque, M. N., Hellwing, A. L. F., Huhtanen, P., Kreuzer, M., Kuhla, B., Lund, P., Madsen, J., Martín, C., McClelland, S. C., McGee, M., Moate, P.J., Muetzel, S., Muñoz, C., O'Kiely, P., Peiren, N., Reynolds, C. K., Schwarm, A., Shingfield, K. J., Storlien, T.M., Weisbjerg, M.R., Yáñez Ruiz, David R., Yu, Z.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/176365
Acceso en línea:http://hdl.handle.net/10261/176365
Access Level:acceso abierto
Palabra clave:Methane intensity
prediction models
Methane yield
Dairy cows
Dry matter in take
Enteric methane emissions
Descripción
Sumario:Enteric methane (CH) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH emission conversion factors for specific regions are required to improve CH production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH yield and intensity prediction, information on milk yield and composition is required for better estimation.

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